Brain-machine interfaces (BMIs) translate action potentials from cortical neurons into control signals for guiding prosthetic devices, such as computer cursors and robotic limbs, offering disabled patients greater interaction with the world. BMIs have recently demonstrated considerable promise in proof-of-concept laboratory animal experiments, as well as in human clinical trials. However, two critical barriers to successful translation remain. First, current BMIs move considerably slower and less accurately than the native arm. Second, they do not sustain performance across hours and days, or across behavioral tasks, without human intervention. The present invention addresses this need for increased performance and robustness and advances the art of neural prosthetics.